Machine learning models may be trained to analyze information for particular purposes involving identifying correlations and making predictions. During training, the models may learn to include illegitimate, non-useful, irrelevant, misleading, or otherwise undesired factors, especially if such biases are present in the training data sets. In particular, while training with structured data involves limiting the data that a model considers, training with unstructured data allows the model to consider all available data, including background information and other undesired factors. For example, a neural network trained with unstructured data including people's appearances to make correlations and predictions about those people may consider such undesired factors as age, sex, ethnicity, and/or race in its subsequent analyses.